Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Vanligaste minsta kvadratmetoden (OLS) Regression× | Panel VAR (Panel Vector Autoregression)× | Kvantilregression× | |
|---|---|---|---|
| Ämnesområde | Ekonometri | Ekonometri | Ekonometri |
| Familj | Regression model | Regression model | Regression model |
| Ursprungsår≠ | 2019 | 1988 | 1978 |
| Upphovsperson≠ | Wooldridge (textbook treatment); classical least squares | Holtz-Eakin, Newey & Rosen | Koenker & Bassett |
| Typ≠ | Linear regression | Panel vector autoregression | Conditional quantile regression |
| Ursprungskälla≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Holtz-Eakin, D., Newey, W. & Rosen, H. S. (1988). Estimating Vector Autoregressions with Panel Data. Econometrica, 56(6), 1371-1395. DOI ↗ | Koenker, R. & Bassett, G., Jr. (1978). Regression Quantiles. Econometrica, 46(1), 33-50. DOI ↗ |
| Alias≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | PVAR, panel vector autoregression, Panel VAR (PVAR) | conditional quantile regression, regression quantiles, Kantil Regresyon |
| Närliggande≠ | 5 | 3 | 5 |
| Sammanfattning≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | Panel VAR extends the vector autoregression model to panel data, modelling the dynamic interactions among several variables while controlling for cross-unit heterogeneity through fixed effects. It was introduced by Holtz-Eakin, Newey and Rosen in 1988 and produces impulse-response functions and variance decompositions at the panel level. | Quantile regression models conditional quantiles of an outcome - the median, the 25th or 75th percentile, and so on - rather than the conditional mean that OLS targets. Introduced by Koenker and Bassett in 1978, it reveals how predictors act across the whole distribution, including its tails. |
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